kotlin-spark-api_3.2.0_2.13/org.jetbrains.kotlinx.spark.api/fullOuterJoin

fullOuterJoin

fun <K, V, W> JavaRDD<Tuple2<K, V>>.fullOuterJoin(other: JavaRDD<Tuple2<K, W>>, partitioner: Partitioner): JavaRDD<Tuple2<K, Tuple2<Optional<V>, Optional<W>>>>

Perform a full outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other, or the pair (k, (Some(v), None)) if no elements in other have key k. Similarly, for each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for v in this, or the pair (k, (None, Some(w))) if no elements in this have key k. Uses the given Partitioner to partition the output RDD.

fun <K, V, W> JavaRDD<Tuple2<K, V>>.fullOuterJoin(other: JavaRDD<Tuple2<K, W>>): JavaRDD<Tuple2<K, Tuple2<Optional<V>, Optional<W>>>>

Perform a full outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other, or the pair (k, (Some(v), None)) if no elements in other have key k. Similarly, for each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for v in this, or the pair (k, (None, Some(w))) if no elements in this have key k. Hash-partitions the resulting RDD using the existing partitioner/ parallelism level.

fun <K, V, W> JavaRDD<Tuple2<K, V>>.fullOuterJoin(other: JavaRDD<Tuple2<K, W>>, numPartitions: Int): JavaRDD<Tuple2<K, Tuple2<Optional<V>, Optional<W>>>>

Perform a full outer join of this and other. For each element (k, v) in this, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other, or the pair (k, (Some(v), None)) if no elements in other have key k. Similarly, for each element (k, w) in other, the resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for v in this, or the pair (k, (None, Some(w))) if no elements in this have key k. Hash-partitions the resulting RDD into the given number of partitions.

fun <K, V, W> JavaDStream<Tuple2<K, V>>.fullOuterJoin(other: JavaDStream<Tuple2<K, W>>, numPartitions: Int = dstream().ssc().sc().defaultParallelism()): JavaDStream<Tuple2<K, Tuple2<Optional<V>, Optional<W>>>>

Return a new DStream by applying 'full outer join' between RDDs of this DStream and other DStream. Hash partitioning is used to generate the RDDs with numPartitions partitions.

fun <K, V, W> JavaDStream<Tuple2<K, V>>.fullOuterJoin(other: JavaDStream<Tuple2<K, W>>, partitioner: Partitioner): JavaDStream<Tuple2<K, Tuple2<Optional<V>, Optional<W>>>>

Return a new DStream by applying 'full outer join' between RDDs of this DStream and other DStream. The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD.